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dc.contributor.author Σταθακοπούλου, Ρεγγίνα el
dc.contributor.author Μαγούλας, Γεώργιος Δ. el
dc.contributor.author Γρηγοριάδου, Μαρίλια el
dc.contributor.author Σαμαράκου, Μαρία el
dc.date.accessioned 2015-04-26T09:44:57Z
dc.date.available 2015-04-26T09:44:57Z
dc.date.issued 2015-04-26
dc.identifier.uri http://hdl.handle.net/11400/8970
dc.rights Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.source http://www.sciencedirect.com/ en
dc.source http://www.sciencedirect.com/science/article/pii/S0020025504000702# en
dc.subject Student diagnosis
dc.subject Uncertainty management
dc.subject Διάγνωση φοιτητών
dc.subject Διαχείρισης αβεβαιότητας
dc.title Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis en
heal.type journalArticle
heal.classification Technology
heal.classification Computer programming
heal.classification Τεχνολογία
heal.classification Προγραμματισμός
heal.classificationURI http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI http://skos.um.es/unescothes/C00749
heal.classificationURI **N/A**-Τεχνολογία
heal.classificationURI **N/A**-Προγραμματισμός
heal.identifier.secondary doi:10.1016/j.ins.2004.02.026
heal.language en
heal.access campus
heal.recordProvider Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε. el
heal.publicationDate 2005
heal.bibliographicCitation Stathacopoulou, R., Magoulas, G., Grigoriadou, M. and Samarakou, M. (February 2005). Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Information Sciences. 170(2-4). pp. 273-307. Elsevier: 2005. Available from: http://www.sciencedirect.com/science/article/pii/S0020025504000702 en
heal.abstract In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent “imitate” teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments. en
heal.publisher Elsevier en
heal.journalName Information Sciences en
heal.journalType peer-reviewed
heal.fullTextAvailability false


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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες